"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"X = train_data.drop([\"Prices\"],axis=1)\n",
"y = train_data[\"Prices\"]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.1, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**训练模型**"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"from flaml import AutoML\n",
"automl = AutoML()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[flaml.automl: 08-14 10:21:47] {2444} INFO - task = regression\n",
"[flaml.automl: 08-14 10:21:47] {2446} INFO - Data split method: uniform\n",
"[flaml.automl: 08-14 10:21:47] {2449} INFO - Evaluation method: cv\n",
"[flaml.automl: 08-14 10:21:47] {2568} INFO - Minimizing error metric: rmse\n",
"[flaml.automl: 08-14 10:21:47] {2708} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']\n",
"[flaml.automl: 08-14 10:21:47] {3010} INFO - iteration 0, current learner lgbm\n",
"[flaml.automl: 08-14 10:21:47] {3143} INFO - Estimated sufficient time budget=310s. Estimated necessary time budget=2s.\n",
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"[flaml.automl: 08-14 10:22:47] {3454} INFO - retrain extra_tree for 0.0s\n",
"[flaml.automl: 08-14 10:22:47] {3461} INFO - retrained model: ExtraTreesRegressor(max_features=0.662808043114978, max_leaf_nodes=51,\n",
" n_estimators=6, n_jobs=-1)\n",
"[flaml.automl: 08-14 10:22:47] {2739} INFO - fit succeeded\n",
"[flaml.automl: 08-14 10:22:47] {2740} INFO - Time taken to find the best model: 29.838769674301147\n"
]
}
],
"source": [
"automl.fit(X, y, task=\"regression\",metric='rmse',time_budget=60*1*1)# 1min"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best ML leaner: extra_tree\n",
"Best hyperparmeter config: {'n_estimators': 6, 'max_features': 0.662808043114978, 'max_leaves': 51}\n",
"Best rmse on validation data: 2.56e+04\n",
"Training duration of best run: 0.02701 s\n"
]
}
],
"source": [
"print('Best ML leaner:', automl.best_estimator)\n",
"print('Best hyperparmeter config:', automl.best_config)\n",
"print('Best rmse on validation data: {0:.4g}'.format(automl.best_loss))\n",
"print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"